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Prostate cancer detection using residual networks

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

To automatically identify regions where prostate cancer is suspected on multi-parametric magnetic resonance images (mp-MRI).

Methods

A residual network was implemented based on segmentations from an expert radiologist on T2-weighted, apparent diffusion coefficient map, and high b-value diffusion-weighted images. Mp-MRIs from 346 patients were used in this study.

Results

The residual network achieved a hit or miss accuracy of 93% for lesion detection, with an average Jaccard score of 71% that compared the agreement between network and radiologist segmentations.

Conclusion

This paper demonstrated the ability for residual networks to learn features for prostate lesion segmentation.

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Fig. 1
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Notes

  1. http://mathworld.wolfram.com/DiskPointPicking.html.

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Acknowledgements

We would like to thank Dr. Fuad Nurili and Dr. Ismail Caymaz for their work in prostate segmentation.

Author information

Authors and Affiliations

  1. Ezra AI Canada, Unit 310, 545 King St. West, Toronto, Canada

    Helen Xu & Diego Cantor-Rivera

  2. Université de Rennes 1, Rennes, France

    John S. H. Baxter

  3. Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Oguz Akin

Authors
  1. Helen Xu
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  2. John S. H. Baxter
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  3. Oguz Akin
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  4. Diego Cantor-Rivera
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Corresponding author

Correspondence to Diego Cantor-Rivera.

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Conflict of interest

Dr. Oguz Akin receives support from Memorial Sloan Kettering Cancer Center Support Grant/Core Grant (P30 CA008748). Dr. Oguz Akin, Dr. Helen Xu and Dr. Diego Cantor-Rivera hold stock options and serve as scientific advisors for Ezra AI Inc., which is developing artificial intelligence algorithms related to the research being reported in this paper.

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Xu, H., Baxter, J.S.H., Akin, O. et al. Prostate cancer detection using residual networks. Int J CARS 14, 1647–1650 (2019). https://doi.org/10.1007/s11548-019-01967-5

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  • DOI: https://doi.org/10.1007/s11548-019-01967-5

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